Recap

  • pivot_longer() helps us take our data from wide to long format
    • names_to = gives a new name to the pivoted columns
    • values_to = gives a new name to the values that used to be in those columns
  • pivot_wider() helps us take our data from long to wide format
    • names_from specifies the old column name that contains the new column names
    • values_from specifies the old column name that contains new cell values
  • to merge/join data sets together need a variable in common - usually “id”

📃Cheatsheet

Recap continued

  • to merge/join data sets together need a variable in common - usually “id”
  • ?join - see different types of joining for dplyr
  • inner_join(x, y) - only rows that match for x and y are kept
  • full_join(x, y) - all rows of x and y are kept
  • left_join(x, y) - all rows of x are kept even if not merged with y
  • right_join(x, y) - all rows of y are kept even if not merged with x
  • anti_join(x, y) - all rows from x not in y keeping just columns from x.
  • esquisser() function of the esquisse package can help make plot sketches

📃Cheatsheet

esquisse and ggplot2

esquisse

ggplot2

Why learn ggplot2?

More customization:

  • branding
  • making plots interactive
  • combining plots

Easier plot automation (creating plots in scripts)

Faster (eventually)

ggplot2

ggplot2

Based on the idea of:

layering

plot objects are placed on top of each other with +

📉 +
📈

ggplot2

ggplotcake

Slide Credit: Tanya Shapiro

ggplot2

  • Pros: extremely powerful/flexible – allows combining multiple plot elements together, allows high customization of a look, many resources online

  • Cons: ggplot2-specific “grammar of graphic” of constructing a plot

  • ggplot2 gallery

Tidy data

To make graphics using ggplot2, our data needs to be in a tidy format

Tidy data:

  1. Each variable forms a column.
  2. Each observation forms a row.

Messy data:

  • Column headers are values, not variable names.
  • Multiple variables are stored in one column.
  • Variables are stored in both rows and columns.

Tidy data: example

Ideally we want each variable as a column and we want each observation in a row.

Column headers are values, not variable names:

Now the the data is “tidy” and in long format

Read more about tidy data and see other examples: Tidy Data tutorial

Data to plot

Type ?er_CO_statewide for more information.

Is the data in tidy? Is it in long format?

er_state <- er_CO_statewide

head(er_state)
# A tibble: 6 × 5
   rate lower95cl upper95cl visits  year
  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
1  6.51      5.80      7.23    323  2011
2  6.58      5.88      7.29    339  2012
3  5.82      5.16      6.49    302  2013
4  4.44      3.87      5.01    237  2014
5  6.55      5.86      7.25    355  2015
6  8.46      7.68      9.23    467  2016

First plot with ggplot2 package

First layer of code with ggplot2 package

Will set up the plot - it will be empty!

First layer of code with ggplot2 package

  • Aesthetic mapping aes(x= , y =) describes how variables in our data are mapped to elements of the plot - Note you don’t need to use mapping but it is helpful to know what we are doing.
library(ggplot2) # don't forget to load ggplot2
# This is not code but shows the general format
ggplot({data_to plot}, aes(x = {var in data to plot},
                                      y = {var in data to plot}))
ggplot(er_state, aes(x = year, y = rate))

Next layer code with ggplot2 package

There are many to choose from, to list just a few:

  • geom_point() – points (we have seen)
  • geom_line() – lines to connect observations
  • geom_boxplot() – boxplots
  • geom_histogram() – histogram
  • geom_bar() – bar plot
  • geom_col() – column plot
  • geom_tile() – blocks filled with color

Next layer code with ggplot2 package

When to use what plot? A few examples:

  • a scatterplot (geom_point()): to examine the relationship between two sets of continuous numeric data
  • a barplot (geom_bar()): to compare the distribution of a quantitative variable (numeric) between groups or categories
  • a histogram (geom_hist()): to observe the overall distribution of numeric data
  • a boxplot (geom_boxplot()): to compare values between different factor levels or categories

Next layer code with ggplot2 package

Need the + sign to add the next layer to specify the type of plot

ggplot({data_to plot}, aes(x = {var in data to plot},
                                      y = {var in data to plot})) +
  geom_{type of plot}</div>
ggplot(er_state, aes(x = year, y = rate)) +
  geom_point()

Read as: using CO statewide ER heat visits data, and provided aesthetic mapping, add points to the plot

Tip - plus sign + must come at end of line

Having the + sign at the beginning of a line will not work!

ggplot(er_state, aes(x = year,
                           y = rate,
                           fill = item_categ))  
 + geom_boxplot()

Pipes will also not work in place of +!

ggplot(er_state, aes(x = year,
                           y = rate,
                           fill = item_categ))  %>%
geom_boxplot()

Plots can be assigned as an object

plt1 <- ggplot(er_state, aes(x = year, y = rate)) +
          geom_point()

plt1

Examples of different geoms

plt1 <- ggplot(er_state, aes(x = year, y = rate)) +
          geom_point()

plt2 <- ggplot(er_state, aes(x = year, y = rate)) +
          geom_line()

plt1 # fig.show = "hold" makes plots appear
plt2 # next to one another in the chunk settings

Specifying plot layers: combining multiple layers

Layer a plot on top of another plot with +

ggplot(er_state, aes(x = year, y = rate)) +
  geom_point() +
  geom_line()

Adding color - can map color to a variable

set.seed(123)
er_visits_4 <- er_CO_county %>% 
  filter(county %in% c("Denver", "Weld", "Pueblo", "Jackson"))

ggplot(er_visits_4, aes(x = year, y = rate, color = county)) +
  geom_point() +
  geom_line()

Adding color - or change the color of each plot layer

You can change look of each layer separately. Note the arguments like linetype and alpha that allow us to change the opacity of the points and style of the line respectively.

ggplot(er_state, aes(x = year, y = rate)) +
  geom_point(size = 5, color = "red", alpha = 0.5) +
  geom_line(size = 0.8, color = "black", linetype = 2)

linetype can be given as a number. See the docs for what numbers correspond to what linetype!

Customize the look of the plot

Customize the look of the plot

You can change the look of whole plot using theme_*() functions.

ggplot(er_state, aes(x = year, y = rate)) +
  geom_point(size = 5, color = "green", alpha = 0.5) +
  geom_line(size = 0.8, color = "blue", linetype = 2) +
  theme_dark()

More themes!

Customize the look of the plot

You can change the look of whole plot - specific elements, too - like changing font and font size - or even more fonts

ggplot(er_state, aes(x = year, y = rate)) +
  geom_point(size = 5, color = "green", alpha = 0.5) +
  geom_line(size = 0.8, color = "blue", linetype = 2) +
  theme_bw() +
  theme(text=element_text(size=16,  family="Comic Sans MS"))

Adding labels

The labs() function can help you add or modify titles on your plot. The title argument specifies the title. The x argument specifies the x axis label. The y argument specifies the y axis label.

ggplot(er_state, aes(x = year, y = rate)) +
            geom_point(size = 5, color = "red", alpha = 0.5) +
            geom_line(size = 0.8, color = "brown", linetype = 2) +
            labs(title = "My plot of Heat-Related ER Visits in CO",
              x = "Year",
              y = "Age-adjusted Visit Rate")

Adding labels line break

Line breaks can be specified using \n within the labs() function to have a label with multiple lines.

ggplot(er_state, aes(x = year, y = rate)) +
            geom_point(size = 5, color = "red", alpha = 0.5) +
            geom_line(size = 0.8, color = "brown", linetype = 2) + 
  labs(title = "My plot of Heat-Related ER Visits in CO: \n age-adjusted visit rate by year",
              x = "Year",
              y = "Age-adjusted Visit Rate")

Changing axis: specifying axis scale

scale_x_continuous() and scale_y_continuous() can change how the axis is plotted. Can use the breaks argument to specify how you want the axis ticks.

range(pull(er_visits_4, year))
[1] 2011 2022
plot_scale <- ggplot(er_state, aes(x = year, y = rate)) + 
                geom_point(size = 5, color = "green", alpha = 0.5) + 
                geom_line(size = 0.8, color = "blue", linetype = 2) +
                scale_x_continuous(breaks = seq(from = 2011, to = 2022, by = 1))
plot_scale

Changing axis: specifying axis limits

xlim() and ylim() can specify the limits for each axis

ggplot(er_state, aes(x = year, y = rate)) +
            geom_point(size = 5, color = "green", alpha = 0.5) +
            geom_line(size = 0.8, color = "blue", linetype = 2) + 
  labs(title = "My plot of Heat-Related ER Visits in CO",
              x = "Year",
              y = "Age-adjusted Visit Rate") +
  ylim(0, max(pull(er_visits_4, rate)))

Modifying plot objects

You can add to a plot object to make changes! Note that we can save our plots as an object like plt1 below. And now if we reference plt1 again our plot will print out!

plt1 <- ggplot(er_state, aes(x = year, y = rate,)) +
            geom_point(size = 5, color = "green", alpha = 0.5) +geom_line(size = 0.8, color = "blue", linetype = 2) + 
  labs(title = "My plot of Heat-Related ER Visits in CO", x = "Year", y = "Age-adjusted Visit Rate")

plt1 + theme_minimal()

Removing the legend label

You can use theme(legend.position = "none") to remove the legend.

er_visits_4 %>% ggplot(aes(x = year,
                      y = rate,
                      color = county)) +
  geom_line(size = 0.8) + 
  theme(legend.position = "none")

Overwriting specifications

It’s possible to go in and change specifications with newer layers Here is our original plot.

er_visits_4 %>% ggplot(aes(x = year,
                      y = rate,
                      color = county)) +
  geom_line(size = 0.8)

Overwriting specifications

It’s possible to go in and change specifications with newer layers

er_visits_4 %>% ggplot(aes(x = year,
                      y = rate,
                      color = county)) +
  geom_line(size = 0.8, color = "black")

Summary

  • ggplot() specifies what data use and what variables will be mapped to where
  • inside ggplot(), aes(x = , y = , color =) specify what variables correspond to what aspects of the plot in general
  • layers of plots can be combined using the + at the end of lines
  • special theme_*() functions can change the overall look
  • individual layers can be customized using arguments like: size, color alpha (more transparent is closer to 0), and linetype
  • labels can be added with the labs() function and x, y, title arguments - the \n can be used for line breaks
  • xlim() and ylim() can limit or expand the plot area
  • scale_x_continuous() and scale_y_continuous() can modify the scale of the axes
  • by default, ggplot() removes points with missing values from plots.

Lab 1

theme() function:

The theme() function can help you modify various elements of your plot. Here we will adjust the font size of the plot title.

ggplot(er_state, aes(x = year, y = rate)) +
            geom_point(size = 5, color = "green", alpha = 0.5) +
            geom_line(size = 0.8, color = "blue", linetype = 2) + 
  labs(title = "My plot of Heat-Related ER Visits in CO") +
  theme(plot.title = element_text(size = 20))

theme() function

The theme() function always takes:

  1. an object to change (use ?theme() to see - plot.title, axis.title, axis.ticks etc.)
  2. the aspect you are changing about this: element_text(), element_line(), element_rect(), element_blank()
  3. what you are changing:
    • text: size, color, fill, face, alpha, angle
    • position: "top", "bottom", "right", "left", "none"
    • rectangle: size, color, fill, linetype
    • line: size, color, linetype

theme() function: center title and change size

The theme() function can help you modify various elements of your plot. Here we will adjust the horizontal justification (hjust) of the plot title.

ggplot(er_state, aes(x = year, y = rate)) +
            geom_point(size = 5, color = "green", alpha = 0.5) +
            geom_line(size = 0.8, color = "blue", linetype = 2) + 
  labs(title = "My plot of Heat-Related ER Visits in CO") +
  theme(plot.title = element_text(hjust = 0.5, size = 20))

theme() function: change title and axis format

ggplot(er_state, aes(x = year, y = rate)) +
            geom_point(size = 5, color = "green", alpha = 0.5) +
            geom_line(size = 0.8, color = "blue", linetype = 2) + 
  labs(title = "My plot of Heat-Related ER Visits in CO") +
  theme(plot.title = element_text(hjust = 0.5, size = 20),
        axis.title = element_text(size = 16))

theme() function: moving (or removing) legend

If specifying position - use: “top”, “bottom”, “right”, “left”, “none”

ggplot(er_visits_4, aes(x = year, y = rate, color = county)) +
  geom_line()

ggplot(er_visits_4, aes(x = year, y = rate, color = county)) +
  geom_line() +
  theme(legend.position = "bottom")

Cheatsheet about theme

Keys for specifications

Linetype key

  • geoms that draw lines have a linetype parameter
  • these include values that are strings like “blank”, “solid”, “dashed”, “dotdash”, “longdash”, and “twodash”
er_state %>% ggplot(aes(x = year,
                      y = rate)) +
  geom_line(size = 0.8, linetype = "twodash")

Keys for specifications

shape key

  • geoms that draw have points have a shape parameter
  • these include numeric values (don’t need quotes for these) and some characters values (need quotes for these)
er_state %>% ggplot(aes(x = year,
                      y = rate)) +
  geom_point(size = 2, shape = 12)

Can make your own theme to use on plots!

Starting a plot

Let’s start with er_visits_4.

ggplot(er_visits_4, aes(x = year,
                 y = rate)) +
  geom_line()

If it looks confusing to you, try again

Using group in plots

You can use group element in a mapping to indicate that each county will have a rate line.

ggplot(er_visits_4, aes(x = year,
                           y = rate,
                           group = county)) +
  geom_line()

Adding color will automatically group the data

ggplot(er_visits_4, aes(x = year,
                           y = rate,
                       color = county)) +
  geom_line()+
  theme(legend.position = "bottom")

Tips!

Let’s talk additional tricks and tips for making ggplots!

We are going to use some other data about ER visits that has to do with gender. Note that gender was recorded as binary, which we know isn’t really accurate. This is something you might encounter. Please see this article about ways to measure gender in a more inclusive way: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6526522/.

Tips - Color vs Fill

  • color is needed for points and lines
  • fill is generally needed for boxes and bars
er_visits_gender <- CO_heat_ER_bygender
ggplot(er_visits_gender, aes(x = gender,
                 y = rate,
                 color = gender)) + #color creates an outline
  geom_boxplot()

ggplot(er_visits_gender, aes(x = gender,
                 y = rate,
                 fill = gender)) + # fills the boxplot
  geom_boxplot()

Tip - Good idea to add jitter layer to top of box plots

Can add width argument to make the jitter more narrow.

ggplot(er_visits_gender, aes(x = gender,
                 y = rate,
                 fill = gender)) +
  geom_boxplot() +
  geom_jitter(width = .06)

Tip - be careful about colors for color vision deficiency

scale_fill_viridis_d() for discrete /categorical data scale_fill_viridis_c() for continuous data

ggplot(er_visits_gender, aes(x = gender,
                 y = rate,
                 fill = gender)) +
  geom_boxplot() +
  geom_jitter(width = .06) +
  scale_fill_viridis_d()

Tip - can pipe data after wrangling into ggplot()

er_bar <- er_visits_gender %>%
  group_by(gender) %>%
  summarize("max_rate" = max(rate, na.rm=T)) %>%

ggplot(aes(x = gender,
           y = max_rate,
           fill = gender)) +
  scale_fill_viridis_d()+
  geom_col() +
  theme(legend.position = "none")

er_bar

Tip - color outside of aes()

Can be used to add an outline around column/bar plots.

er_bar +
   geom_col(color = "black")

Tip - col vs bar

geom_bar(x =) can only use one aes mapping geom_col(x = , y = ) can have two

Tip - Check what you plot

⚠️ May not be plotting what you think you are! ⚠️

ggplot(er_visits_gender, aes(x = gender,
                 y = visits,
                 fill = gender)) +
  geom_col()

What did we plot? Always good to check it is correct!

head(er_visits_gender, n = 3)
# A tibble: 3 × 7
  county  rate lower95cl upper95cl visits  year gender
  <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl> <chr> 
1 Adams   7.60      4.38     11.7      17  2011 Female
2 Adams  NA        NA        NA        NA  2012 Female
3 Adams   6.22      3.37      9.93     14  2013 Female
er_visits_gender %>% group_by(gender) %>%
  summarize(sum = sum(visits, na.rm=T))
# A tibble: 2 × 2
  gender   sum
  <chr>  <dbl>
1 Female  2556
2 Male    4331

Try that again

er_visits_gender %>% group_by(gender, county) %>%
  summarize(mean_visits = mean(visits, na.rm=T))
# A tibble: 20 × 3
# Groups:   gender [2]
   gender county    mean_visits
   <chr>  <chr>           <dbl>
 1 Female Adams            15.8
 2 Female Arapahoe         14.4
 3 Female Cheyenne          0  
 4 Female Denver           14.4
 5 Female El Paso          15.3
 6 Female Jefferson        14.1
 7 Female Larimer          13.5
 8 Female Pueblo           12.7
 9 Female Statewide       142. 
10 Female Weld             15  
11 Male   Adams            18.9
12 Male   Arapahoe         17.3
13 Male   Cheyenne          0  
14 Male   Denver           22.5
15 Male   El Paso          23.1
16 Male   Jefferson        16.3
17 Male   Larimer          20.7
18 Male   Pueblo           17.1
19 Male   Statewide       225. 
20 Male   Weld             17.5

Try that again

er_visits_gender %>% group_by(gender, county) %>%
  summarize(mean_visits = mean(visits, na.rm=T)) %>%

ggplot(aes(x = gender,
           y = mean_visits,
           fill = gender)) +
  geom_col()

Tip - make sure labels aren’t too small

er_bar +
  theme(text = element_text(size = 20))

Tip- if you need you can remove outliers

Set outlier.shape = NA to get ride of outliers. Be careful about if you really should remove these!

However, if can be helpful if your plot is getting stretched to accommodate plotting an outlier. You can always say in the figure legend what you removed.

er_no_out1 <- ggplot(er_visits_gender, aes(y = visits, x = gender)) +
  geom_boxplot()

er_no_out2 <- ggplot(er_visits_gender, aes(y = visits, x = gender)) +
  geom_boxplot(outlier.shape = NA) +
  ylim(0,40)

NA Values

  • if it is a numeric value it will just get dropped from the graph and you will see a warning
  • it is categorical you will see it on the graph and will need to filter to remove the NA category
icecream <-tibble(flavor =
            rep(c("chocolate", "vanilla", NA,"chocolate", "vanilla"), 8))

icecream1 <- ggplot(icecream, aes(x = flavor)) + geom_bar() +
              theme(text=element_text(size=24))

icecream2 <- icecream %>% drop_na(flavor)  %>%
                ggplot( aes(x = flavor)) + geom_bar() +
                  theme(text=element_text(size=24))

## Sometimes we have many lines and it is hard to see what is happening{.codesmall}

er_visits_9 <- er_CO_county %>% 
  filter(county %in% c("Denver", "Weld", "Pueblo", "Jackson", 
                       "San Juan", "Mesa", "Jefferson", "Larimer", "Statewide"))

lots_of_lines <- ggplot(er_visits_9, aes(x = year,
                 y = rate,
                 color = county)) +
                 geom_line()
lots_of_lines

Adding a facet can help make it easier to see what is happening

Sometimes we have two many lines and can git difficult to see what is happening, facets can help!

Two options: facet_grid()- creates a grid shape facet_wrap() -more flexible

Need to specify how you are faceting with the ~ sign.

lots_of_lines +
facet_grid( ~ county) +
theme(legend.position = "bottom")

Adding a facet can help make it easier to see what is happening

lots_of_lines +
facet_grid( ~ county) +
theme(legend.position = "none") + 
theme(axis.text.x = element_text(angle = 90))

facet_wrap()

  • more flexible - arguments ncol and nrow can specify layout
  • can have different scales for axes using scales = "free"
rp_fac_plot <- lots_of_lines +
               facet_wrap( ~ county, ncol = 4, scales = "free") +
               theme(legend.position = "none")
rp_fac_plot

Extensions

directlabels package

patchwork package

Great for combining plots together

Also check out the patchwork package

#install.packages("patchwork")
library(patchwork)
(plt1 + plt2)/plt2

plotly

#install.packages("plotly")
library("plotly") # creates interactive plots!
ggplotly(lots_of_lines)

Also check out the ggiraph package

Saving plots

Saving a ggplot to file

A few options:

  • RStudio > Plots > Export > Save as image / Save as PDF
  • RStudio > Plots > Zoom > [right mouse click on the plot] > Save image as
  • In the code
ggsave(filename = "saved_plot.png",  # will save in working directory
       plot = rp_fac_plot,
       width = 6, height = 3.5)               # by default in inches

Summary

  • The theme() function helps you specify aspects about your plot
    • move or remove a legend with theme(legend.position = "none")
    • change font aspects of individual text elements theme(plot.title = element_text(size = 20))
    • center a title: theme(plot.title = element_text(hjust = 0.5))
  • sometimes you need to add a group element to aes() if your plot looks strange
  • make sure you are plotting what you think you are by checking the numbers!
  • facet_grid(~ variable) and facet_wrap(~variable) can be helpful to quickly split up your plot
    • facet_wrap() allows for a scales = "free" argument so that you can have a different axis scale for different plots
  • use fill to fill in boxplots

Good practices for plots

Check out this guide for more information!

Lab 2